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Year Published

550 Results

July 25, 2016

Single Image 3D Interpreter Network

European Conference on Computer Vision (ECCV)

In this work, we propose 3D INterpreter Network (3D-INN), an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data.

By: Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman
July 23, 2016

HapticWave: Directional Surface Vibrations using Wave-Field Synthesis

SIGGRAPH 2016

HapticWave is a novel haptic technology that delivers directional haptic sensations generated on a flat surface to the user, without requiring him/her to wear a physical device.

By: Ravish Mehra, Christoph Hohnerlein, David Perek, Elia Gatti, Riccardo DeSalvo, Sean Keller
July 19, 2016

Luminescent Detector for Free-Space Optical Communication

Optica 3, 787-792 (2016)

We show that fluorescent materials can be used to increase the active area of a photodiode by orders of magnitude while maintaining its short response time and increasing its field of view.

By: Thibault Peyronel, Kevin Quirk, S. C. Wang, Tobias Tiecke
June 27, 2016

Unsupervised Learning of Edges

CVPR

Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries.

By: Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollar
June 26, 2016

End-to-End Voxel-to-Voxel Prediction

Conference on Computer Vision and Pattern Recognition (CVPR)

Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis with most successful applications having been in the area of video classification and detection. In this paper we challenge these views by presenting a deep 3D convolutional architecture trained end to end to perform voxel-level prediction, i.e., to output a variable at every voxel of the video.

By: Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
June 25, 2016

Realtime Data Processing at Facebook

ACM SIGMOD

Realtime data processing powers many use cases at Facebook, including realtime reporting of the aggregated, anonymized voice of Facebook users, analytics for mobile applications, and insights for Facebook page administrators.

By: Guoqiang Jerry Chen, Janet Wiener, Shridhar Iyer, Anshul Jaiswal, Ran Lei, Nikhil Simha, Wei Wang, Kevin Wilfong, Tim Williamson, Serhat Yilmaz
June 20, 2016

Combining Two and Three-Way Embeddings Models for Link Prediction in Knowledge Bases

Journal of Artificial Intelligence Research, JAIR.org

This paper tackles the problem of endogenous link prediction for Knowledge Base completion.

By: Alberto Garcia-Duran, Antoine Bordes, Nicolas Usunier, Yves Grandvalet
June 19, 2016

Recurrent Orthogonal Networks and Long-Memory Tasks

International Conference on Machine Learning

This paper analyzes two synthetic datasets originally outlined in (Hochreiter and Schmidhuber, 1997) which are used to evaluate the ability of RNNs to store information over many time steps and explicitly construct RNN solutions to these problems.

By: Mikael Henaff, Arthur Szlam, Yann LeCun
June 18, 2016

Dynamo: Facebook’s Data Center-Wide Power Management System

ISCA 2016

In this paper, we describe Dynamo – a data center-wide power management system that monitors the entire power hierarchy and makes coordinated control decisions to safely and efficiently use provisioned data center power.

By: Qiang Wu, Qingyuan Deng, Lakshmi Ganesh, Chang-Hong Raymond Hsu, Yun Jin, Sanjeev Kumar, Bin Li, Justin Meza, Yee Jiun Song
June 18, 2016

Learning Physical Intuition of Block Towers by Example

International Conference on Machine Learning

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.

By: Adam Lerer, Sam Gross, Rob Fergus